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Kat Holder
Kat Holder

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Most Productive Machine Learning Tools For 2021

Machine Learning is a twenty-first century’s necessary approach in any industry that deals with data. With data or rather big data that has ushered in every sphere of industry, one needs tools to analyze and make better and more strategic decisions that promise results. One needs to understand several factors- consumer behavior, happiness, and loyalty. Machine Learning is a science of patterns in information, in a layman’s language, and with that, machine learning tools help you figure out these patterns. The tools help in creating personal machine learning methods
for any data, the user would have.

Best Machine Learning Tools

Amazon Machine Learning

One wonders whether it is an act of philanthropy or practice in making sure the world of AI never stops exploring itself that Amazon Machine Learning software intends to give access to machine learning capabilities to anyone who wants to have it. It aids businesses of all sizes and helps them reach their potential.
Companies can easily construct, train, and employ multiple machine learning services. By doing so, it removes the complexity from each step of the ML workflow so that one can easily use it and it would help in predicting the consumers; behavior to make analysis. One can use case solutions that would engage AI and ML services for business outcomes. It also integrates AI into the existing system. Through this, it aims to improve customer experience, accelerate innovation and make progress in business operations.

Jupyter Notebook

It is one of the most popular machine learning tools out there, that combines ultra-fast processing rates. JupyterLab, i.e.- Jupyter’s Next-Generation Notebook Interface is flexible and arranges the user interface that provides support to several types of workflows running from data science to machine learning to scientific computing. It is extensible and modular with the flexibility of integrating new components with the existing ones. Along with this, it supports over 40 languages, and Python, Scala, and R are amongst them.

KNIME

It shares its features with the above-mentioned tools in terms of Open-Source Software to the accessibility it provides. It is an active participant in the entire data science life cycle- it controls the flow of data, and also ensures that the work is updated. Also, it has blend tools segregated from different domains that work with KNIME in a single workflow, and also connects with Apache Spark.
The best part of this is that you can start without needing any programming experience in it. You can easily use the data technology that it provides to make sense of your statistics and based on that knowledge, make informed ML algorithms that would help in making you get better analytics and decisions.

Azure Machine Learning Studio

It is visual machine learning that accelerates productivity. It has a drag and drops feature that speeds up model building and deployment for the entire team irrespective of the expertise level of the data scientists. One can prepare and preprocess data using the built-in modules and build, train models visually using the latest algorithms that are in the ML.
Also, it stores models and other assets in the central agency for machine learning operations and tracking and lineage. It is a simple-to-use application that aids in connecting modules and datasets of businesses into a proper plan for developing ML technology.

KNIME

It shares its features with the above-mentioned tools in terms of open-source software to the accessibility it provides. It is an active participant in the entire data science life cycle it controls the flow of data, and also ensures that the work is updated. Also, it has blend tools segregated from different domains that work with KNIME in a single workflow, and also connects with Apache Spark.
The best part of this is that you can start without needing any programming experience in it. You can easily use the data technology that it provides to make sense of your statistics and based on that knowledge, make informed ML algorithms that would help in making you get better analytics and decisions.

Shogun

It is a library that is easily accessible to anyone and an open-source tool for ML problems. It was made public in 2004. The user can use it among various enterprises of various sizes and backgrounds. It is mostly written in C++ language with a very low number of source code comments. Because of the language it is written in, it becomes available in a number of other programming languages like Ruby, Scala, Python. A well-established and mature codebase that is maintained by a large development team with stable Y-O-Y commits.

It also involves a number of methods and data structures that are used to investigate typical machine learning issues. It also involves features like vector machine functionality to an existing tool and an advanced user interface that makes the job simpler.

WEKA

Optimal for a beginner, this acronym stands for Waikato Environment for Knowledge Analysis. It is used for a graphical user interface, conventional terminal program, and a Java API. It is also used for making robust applications, research, and teaching I models.
Because it provides you with in-built tools, it is a good one to start with if you are a beginner. You also have access to other well-known toolboxes such as Scikit-Learn.

TensorFlow

Owned by Google, it offers similar features like an open-source framework like its alternatives, it also provides deep learning neural networks with other ML techniques. It comes quite handy for Python users.
The best thing about this is that it can easily operate on both CPU and GPU technologies. It also stands out due to its flexible architecture in taking new ideas and experimenting with them for research.

Scikit Learn

Initially known as scikits.learn and started as Google Summer of Code. It has emerged to be a well-documented Python machine learning library. The library is maintained and reliable, One of the features that help to set out are the tools that include everything from dataset loading and manipulation to preprocessing pipelines and metrics. It involves minimal code adjustments. It involves simple and efficient tools for predictive data analysis. It is open-source and commercially usable.
It is accessible to everyone and reusable in various contexts. Accessible for a range of data management, it gauges its popularity from having several features like- regression, categorization, clustering, and pre-processing capabilities.

Conclusion

The demand for this technology will only rise as our dependency on the same is increasing and there is not going to be a change in the matter in any manner. If you can suggest any or provide any new insights regarding any of them, it would be received with open arms.

Discussion (1)

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Matias Loiseau

Thanks for the post, was a good reading